Towards learning inverse kinematics with a neural network based tracking controller

Learning an inverse kinematic model of a robot is a well studied subject. However, achieving this without information about the geometric characteristics of the robot is less investigated. In this work, a novel control approach is presented based on a recurrent neural network. Without any prior knowledge about the robot, this control strategy learns to control the iCub’s robot arm online by solving the inverse kinematic problem in its control region. Because of its exploration strategy the robot starts to learn by generating and observing random motor behavior. The modulation and generalization capabilities of this approach are investigated as well

Ecogeographical rules and the macroecology of food webs

AimHow do factors such as space, time, climate and other ecological drivers influence food web structure and dynamics? Collections of well‐studied food webs and replicate food webs from the same system that span biogeographical and ecological gradients now enable detailed, quantitative investigation of such questions and help integrate food web ecology and macroecology. Here, we integrate macroecology and food web ecology by focusing on how ecogeographical rules [the latitudinal diversity gradient (LDG), Bergmann’s rule, the island rule and Rapoport’s rule] are associated with the architecture of food webs.LocationGlobal.Time periodCurrent.Major taxa studiedAll taxa.MethodsWe discuss the implications of each ecogeographical rule for food webs, present predictions for how food web structure will vary with each rule, assess empirical support where available, and discuss how food webs may influence ecogeographical rules. Finally, we recommend systems and approaches for further advancing this research agenda.ResultsWe derived testable predictions for some ecogeographical rules (e.g. LDG, Rapoport’s rule), while for others (e.g., Bergmann’s and island rules) it is less clear how we would expect food webs to change over macroecological scales. Based on the LDG, we found weak support for both positive and negative relationships between food chain length and latitude and for increased generality and linkage density at higher latitudes. Based on Rapoport’s rule, we found support for the prediction that species turnover in food webs is inversely related to latitude.Main conclusionsThe macroecology of food webs goes beyond traditional approaches to biodiversity at macroecological scales by focusing on trophic interactions among species. The collection of food web data for different types of ecosystems across biogeographical gradients is key to advance this research agenda. Further, considering food web interactions as a selection pressure that drives or disrupts ecogeographical rules has the potential to address both mechanisms of and deviations from these macroecological relationships. For these reasons, further integration of macroecology and food webs will help ecologists better understand the assembly, maintenance and change of ecosystems across space and time.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/151318/1/geb12925_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/151318/2/geb12925.pd

Motion Pattern Encapsulation for Data-Driven Constraint-Based Motion Editing

The growth of motion capture systems have contributed to the proliferation of human motion database, mainly because human motion is important in many applications, ranging from games entertainment and films to sports and medicine. However, the captured motions normally attend specific needs. As an effort for adapting and reusing captured human motions in new tasks and environments and improving the animator’s work, we present and discuss a new data-driven constraint-based animation system for interactive human motion editing. This method offers the compelling advantage that it provides faster deformations and more natural-looking motion results compared to goal-directed constraint-based methods found in the literature

Reversal of MDR1-associated resistance to topotecan by PAK-200S, a new dihydropyridine analogue, in human cancer cell lines

Recent data suggest that expression of the membrane P170-glycoprotein (P-gp) may confer resistance to the topoisomerase- I-interactive agent topotecan. The present study describes the cellular effects of a new dihydropyridine analogue, PAK-200S, on P-gp-mediated resistance to topotecan in human breast and ovarian tumour cells. PAK-200S at a non-cytotoxic concentration of 2.0 μM completely reversed resistance to topotecan in P-gp-expressing MCF-7/adr (breast) and A2780/Dx5 (ovarian) tumour cells, respectively, with no effects on parental cells. Cellular pharmacokinetic studies by reversed-phase high-performance liquid chromatography analysis showed significantly lower cellular drug concentrations of the pharmacologically active closed-ring lactone of topotecan in multidrug-resistant cells than in parental cells. PAK-200S was effective in restoring the cellular lactone concentrations of topotecan in resistant MCF-7/adr cells to levels comparable to those obtained in parental cells. Furthermore, exposure of MCF-7/adr cells to topotecan in the presence of PAK-200S significantly increased the induction of protein-linked DNA breaks. PAK-200S did not alter nuclear topoisomerase I-mediated ex vivo pBR322 DNA plasmid unwinding activity and topoisomerase-I protein expression. These results suggest that reversal of P-gp-mediated resistance to topotecan by PAK-200S was related to the restoration of cellular drug concentrations of the active lactone form of topotecan rather than a direct effect on topoisomerase-I function. © 1999 Cancer Research Campaig

HUMAN MOTION RECONSTRUCTION BY DIRECT CONTROL OF MARKER TRAJECTORIES

Abstract Understanding the basis of human movement and reproducing it in robotic environments is a compelling challenge that has engaged a multidisciplinary audience. In addressing this challenge, an important initial step involves reconstructing motion from experimental motion capture data. To this end we propose a new algorithm to reconstruct human motion from motion capture data through direct control of captured marker trajectories. This algorithm is based on a task/posture decomposition and prioritized control approach. This approach ensures smooth tracking of desired marker trajectories as well as the extraction of joint angles in real-time without the need for inverse kinematics. It also provides flexibility over traditional inverse kinematic approaches. Our algorithm was validated on a sequence of tai chi motions. The results demonstrate the efficacy of the direct marker control approach for motion reconstruction from experimental marker data